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Power Input Mapping for Vibro-Acoustic Design

Université de Sherbrooke-walid Belgacem, Noureddine Atalla, Malek Khalladi
  • Technical Paper
  • 2020-01-1576
To be published on 2020-06-03 by SAE International in United States
The input mobility is an important vibro-acoustic parameter used by engineers in the industrial design process. In fact, this information guides the choice of the connection between the vibrational source and the receiver. To select the most effective connection points, the input mobility is characterized at every possible location of the receiver structure leading to a mapping of the input mobility. Several works propose to compute the full map by averaging the input mobility in a given frequency bands over a Finite Elements (FE) mesh of the receiver structure. By nature, the input mobility is a Frequency Response Function (FRF); consequently, it does not consider the frequency content of the source. This paper presents a method to compute a full map of input power instead of input mobility. The proposed method uses a modal decomposition on the receiver structure, source frequency behaviour and frequency integration by introducing frequency weighting coefficients (Human vibration perception and source cycle use in real conditions). Thus, a single map is provided, that condensate the information of the input power, for…
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Unsettled Topics on the Feasibility and Desirability of Using AM in the Mobility Industry

The Barnes Global Advisors-Kevin Slattery
  • Research Report
  • EPR2020009
To be published on 2020-04-24 by SAE International in United States
Depending on the industry and application, views on additive manufacturing (AM), or “3D printing,” range from something that will transform an industry to it being another over-hyped technology that will only find niche applications. Most views fall somewhere in between, with the most common one being that it depends on the application and technology. Because of the ability to directly produce parts from a digital file, views often include dependence on when and where the part is needed. This introduces the crux of the matter, which is how to determine when the use of AM is feasible and desirable, which is made all the more complicated by the fact that not only is AM technology in general changing quickly, but the merits of the each AM technology relative to the others is also changing. Finally, non-AM technologies are continually improving, and are increasingly adding AM-like capability. As the opening report of a four-part series of SAE EDGE™ Research Reports on AM, this paper discusses unsettled issues pertaining to the benefits, drawbacks, and trade-offs, as well…
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Traffic Control Strategies for Congested Heterogeneous Multi-Vehicle Networks

University of North Carolina Charlotte-Pouria Karimi Shahri, Amir H. Ghasemi, Vahid Izadi
  • Technical Paper
  • 2020-01-0086
To be published on 2020-04-14 by SAE International in United States
The primary goal of this paper is to pioneer and develop robust and adaptive algorithms for controlling autonomous vehicles in heterogeneous networks with the aim of maximizing the performance (in terms of mobility) and minimizing variation in the network. While the fundamental approaches and models proposed in this research can be applied to any heterogeneous multi-agent system, we select heterogeneous traffic networks as a set-up for exploring the proposed research. We consider the heterogeneity in the system in the form of a mix of autonomous and human-driven vehicles (different levels of autonomous vehicle penetration). We propose a two-level hierarchical controller wherein the upper-level controller, an optimization problem using the concept of macroscopic fundamental diagram is formulated to deal with the traffic demand balance problem. At the lower level, using the microscopic models of the network, the control actions for each vehicle will be determined such that he optimal flow received from the upper-level controllers can be tracked.
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RouteE: A Vehicle Energy Consumption Prediction Engine

National Renewable Energy Laboratory-Jacob Holden, Nicholas Reinicke, Jeff Cappellucci
  • Technical Paper
  • 2020-01-0939
To be published on 2020-04-14 by SAE International in United States
The emergence of Connected and Automated Vehicles and Smart Cities technologies create the opportunity for new mobility mode and routing decision tools, among many others. In order to achieve maximal mobility and minimal energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy Prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data is unavailable. Applications include vehicle route selection, energy accounting/optimization in transportation simulation, and corridor energy analyses, among others. The software is an open-source Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own datasets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are trained using NREL’s Future Automotive Systems Technology Simulator (FASTSim) paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center (TSDC) resulting…
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Understanding Charging Flexibility of Shared Autonomous Electric Vehicle Fleets

National Renewable Energy Laboratory-Matthew Moniot, Yanbo Ge, Nicholas Reinicke, Alex Schroeder
  • Technical Paper
  • 2020-01-0941
To be published on 2020-04-14 by SAE International in United States
The combined anticipated trends of vehicle sharing, autonomous control, and powertrain electrification are poised to disrupt the current paradigm of predominately gasoline vehicles with low levels of utilization. Shared, autonomous, electric vehicle (SAEV) fleets, which encompass all three of these trends, have garnered significant interest among the research community due to the opportunity for low-cost mobility with congestion and emissions reductions. This paper explores the charging loads demanded by SAEV fleets in response to servicing personal light-duty vehicle travel demand in four major United States metropolitan areas: Detroit, Austin, Washington DC, and Miami. A coordinated charging model is introduced which minimizes fleet charging costs and corresponding plant emissions in response to different renewable energy penetration rates and shares of personal trip demand served (between 1% and 25%). The relationship between trip demand by time of day, electricity price by time of day, and SAEV fleet size versus overall charging flexibility is explored for each city. SAEV results are presented across various scenarios assuming fleetwide attempts to minimize charging costs while still constrained by offering adequate…
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An Electric Motor Thermal Bus Cooling System for Vehicle Propulsion - Design and Test

CCDC Ground Vehicle Systems Center-Katherine Sebeck
Clemson University-Shervin Shoai Naini, Richard Miller, John Wagner
  • Technical Paper
  • 2020-01-0745
To be published on 2020-04-14 by SAE International in United States
Automotive and truck manufacturers are introducing electric propulsion systems into their ground vehicles to reduce fossil fuel consumption and harmful tailpipe emissions. The mobility shift to electric motors requires a compact thermal management system that can accommodate heat dissipation demands with minimum energy consumption in a confined space. An innovative cooling system design, emphasizing passive cooling methods coupled with a small liquid system, using a thermal bus architecture has been explored. The laboratory experiment features an emulated electric motor interfaced to a thermal cradle and multiple heat rejection pathways to evaluate the transfer of generated heat to the ambient surroundings. The thermal response of passive (e.g., carbon fiber, high thermal conductivity material, thermosyphon) and active cooling systems are investigated for two operating scenarios. The test results demonstrate that up to 93% improvement can be achieved in cooling system energy consumption during a light load electric motor condition while maintaining a target core temperature of 70°C. The governing thermal system dynamics will be reviewed in discussion of the experimental observations.
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A Decision Based Mobility Model for Semi and Fully Autonomous Vehicles

FCA US LLC-Christopher Slon
Oakland University-Vijitashwa Pandey, Line Deschenes
  • Technical Paper
  • 2020-01-0747
To be published on 2020-04-14 by SAE International in United States
With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs. An example of this is the collection of soil properties data using techniques such as remote sensing. Operators of these vehicles are interested in finding the value of collecting such information.…
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Dyno-in-the-Loop: An Innovative Hardware-in-the-Loop Development and Testing Platform for Emerging Mobility Technologies

Oak Ridge National Laboratory-Zhiming Gao, Tim LaClair
University of California Riverside-Guoyuan Wu, Dylan Brown, Zhouqiao Zhao, Peng Hao, Michael Todd, Kanok Boriboonsomsin, Matthew Barth
  • Technical Paper
  • 2020-01-1057
To be published on 2020-04-14 by SAE International in United States
Today’s transportation is quickly transforming with the nascent advent of connectivity, automation, shared-mobility, and electrification. These technologies will not only affect our safety and mobility, but also our energy consumption, and environment. As a result, it is of unprecedented importance to understand the overall system impacts due to the introduction of these emerging technologies and concepts. Existing modeling tools are not able to effectively capture the implications of these technologies, not to mention accurately and reliably evaluating their effectiveness with a reasonable scope. To address these gaps, a dynamometer-in-the-loop (DiL) development and testing approach is proposed which integrates test vehicle(s), chassis dynamometer, and high fidelity traffic simulation tools, in order to achieve a balance between the model accuracy and scalability of environmental analysis for the next generation of transportation systems. With this DiL platform, a connected eco-operation system for the plug-in hybrid electric bus (PHEB) has been developed and tested, which can optimize the vehicle dynamics (and potentially powertrain control via smart energy management) to reduce the operational energy consumption as well as tailpipe emissions…
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A Study of Using a Reinforcement Learning Method to Improve Fuel Consumption of a Connected Vehicle with Signal Phase and Timing Data

The University of Alabama-Ashley Phan, Hwan-Sik Yoon
  • Technical Paper
  • 2020-01-0888
To be published on 2020-04-14 by SAE International in United States
Connected and automated vehicles (CAVs) promise to reshape two areas of the mobility industry: the transportation and driving experience. The connected feature of the vehicle uses communication protocols to provide awareness of the surrounding world while the automated feature uses technology to minimize driver dependency. Constituting a subset of connected technologies, vehicle-to-infrastructure (V2I) technologies provide vehicles with real-time traffic light information, or Signal Phase and Timing (SPaT) data. In this paper, the vehicle and SPaT data are combined with a reinforcement learning (RL) method as an effort to minimize the vehicle’s energy consumption. Specifically, this paper explores the implementation of the deep deterministic policy gradient (DDPG) algorithm. As an off-policy approach, DDPG utilizes the maximum Q-value for the state regardless of the previous action performed. In this research, the SPaT data collected from dedicated short-range communication (DSRC) hardware installed at 16 real traffic lights is utilized in a simulated road modeled after a road in Tuscaloosa, Alabama. The vehicle is trained using DDPG and the SPaT data to determine the optimal action to take in…
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A Virtual Driveline Concept to Maximize Mobility Performance of Autonomous Electric Vehicles

Alion Science & Technology-Michael Letherwood
U.S. Army Ground Vehicle Systems Center-David Gorsich
  • Technical Paper
  • 2020-01-0746
To be published on 2020-04-14 by SAE International in United States
In-wheel electric motors open up new prospects to radically enhance the mobility of autonomous electric vehicles with four or more driving wheels. The flexibility and agility of delivering torque individually to each wheel can allow significant mobility improvements, agile maneuvers, maintaining stability, and increased energy efficiency. However, the fact that individual wheels are not connected mechanically by a driveline system does not mean their drives do not impact each other. With individual torques, the wheels will have different longitudinal forces and tire slippages. Thus, the absence of driveline systems physically connecting the wheels requires new approaches to coordinate torque distribution. This paper solves two technical problems. First, a virtual driveline system (VDS) is proposed to emulate a mechanical driveline system virtually connecting the e-motor driveshafts, providing coordinated driving wheel torque management. The VDS simulates power split between driving wheels. Conceptually, VDS is founded on generalized tire and vehicle parameters. Generalized slippages are utilized to determine virtual gear ratios from a virtual transfer case to each wheel. The virtual gear ratios serve as signals to the…